<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">T. Natschlaeger</style></author><author><style face="normal" font="default" size="100%">W. Maass</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">S. Thrun</style></author><author><style face="normal" font="default" size="100%">L. Saul</style></author><author><style face="normal" font="default" size="100%">B. Schoelkopf</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Information dynamics and emergent computation in recurrent circuits of spiking neurons</style></title><secondary-title><style face="normal" font="default" size="100%">NIPS 2003: Advances in Neural Information Processing Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2004</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">MIT Press</style></publisher><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">1255-1262</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;An efficient method using Bayesian and linear classifiers is presented for   analyzing the dynamics of information in high dimensional circuit states, and   applied to investigate emergent computation in generic cortical microcircuit   models. It is shown that such recurrent circuits of spiking neurons have an   inherent capability to carry out rapid computations on complex spike   patterns, merging information contained in the order of spike arrival with   previously acquired context information.&lt;/p&gt;</style></abstract></record></records></xml>